Fleet-Oriented Intelligent Operation of Large Scale Edge System

The demand to process data close to where the data is gathered for example in order to gain efficiency, or ensure privacy and security avoiding the need to submit sensitive data over the network to the cloud has lead to the emergence of Edge computing. Edge computing comprises software instances running both in the cloud and on devices at the edge of the network such as gateways, routers, switches, small base stations, etc. While exploiting edge computing represent set of potential gains, the complexity of operating Edge systems represents daunting challenges since software instances are running in geographically distributed devices and each of them has a unique operation context, which is continuously changing and often unpredictable. For large scale Edge systems with thousand devices, operation effort and cost can easily exceed the project's financial benefits. In the Fleet project, we work to solve these issues and are developing a software-based framework with associated tools to solve these complex challenges. Frameworks and tools to solve this is not available on the market today. We have developed a language to be able to specify and design edge computing systems and use a generative approach where the project also develops appropriate tools to support automation of large scale distribution of software on a large number of edge nodes (e.g., GWs distributed in people's homes) and achieve economies of scale for Edge operation. A key challenge that is addressed is how to take automation to the next level by offering intelligent tools that understand the context of devices and continuously optimize operating plans accordingly. In summary the FLEET project is currently developing context-aware intelligent operation of large scale Edge systems, where we aim at delivering a set of software engineering techniques and tools to operate large scale Edge systems. This will include design tools and context aware operation tools to automatically infer how, where and when to deploy and adapt the software running on the individual Edge devices according to their contexts in a secure and trustworthy manner.

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The demand to place data analytics close to where the data is created leads to the emergence of Edge computing systems, which comprises software instances running both in the cloud and on devices at the edge of the network such as gateways, routers, switches, small base stations, etc. However, the complexity of operating Edge systems represents daunting challenges since software instances are running in geographically distributed devices and each of them has a unique operation context, which is continuously changing and often unpredictable. For large scale Edge systems with thousand devices, operation effort and cost can easily exceed the project's financial benefits. Edge computing is still in its infancy and a framework to address this problem is not yet available. The traditional mechanical automation, widely used for Cloud computing, still requires heavy human interaction to handle each device individually. To achieve the economy of scale for Edge operation, we need to take automation to the next level by providing intelligent tools that understand the context of devices and continuously optimize operation plans accordingly by themselves. In FLEET, we propose the novel concept of context-aware intelligent operation of large scale Edge systems. More precisely, we aim at delivering a set of software engineering techniques and tools to operate large scale Edge systems. This will include (i) a novel fleet-oriented modelling language for developers to define overall operation goals to specify how they expect to operate the fleet of Edge devices as a whole, (ii) context-aware intelligent operation agents to automatically infer how, where and when to deploy and adapt the software running on the individual Edge devices according to their contexts, and (iii) veto-based trustworthiness assurance techniques to tame the intelligent operation agents against trustworthiness risks (including security and privacy).